nortrip- 2010 11 16 1 smhi road dust emission model gunnar omstedt, smhi model description and...
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NORTRIP- 2010 11 16
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SMHI road dust emission model Gunnar Omstedt, SMHI
•model description and results
•improvements of existing model
•new parameters
Omstedt, G., Bringfelt, B. och Johansson, C., 2005: A model for vehicle-induced non-tailpipe emissions of particles along Swedish roads. Atmospheric Environment, 39, 6088-6097.
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Model description
)(** int, dustelfe erwrefPMfq
suspensionf
suspensionf
directf
totf eee
Winter
Summer )(* , dustefe summerrefPMfq
suspensionf
fq is the source strength reduction function due to the moisture content of the road (0-1) l is the amount of dust on the road (0-1)
)(,
dustrefPMf
e is a reference emission factor
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l is the dust layer (relative units)
ll SiSot
l
)(
Sources Sinks Sanding Vehicle-induced
suspension Traffic related road wear Run-off with precipitation
g (mm) is the moister amount on the surface
gg SiSot
g
)(
Sources SinksPrecipitation Run-off
Evaporation
Equations 1 and 2 are coupled due to the parameter describing vehicle-induced suspension.
If the surface is wet, only a small fraction of l is emitted, then l is increasing.
If the surface is dry and l is large, a large fraction of l is emitted, then l is decreasing.
(1)
(2)
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Referens emissionfactor:
the baseline for the model is givenby scaling, using NOx as a tracer
backgroundNOx
roadsideNOx
backgroundPM
roadsidePMNOx
fPMf
CC
CCee
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Model description: the fq parameter
htt errgg *),0.1min( 1 t denotes present hour and t-1 previous hourg street moister content (mm)rr is the hourly precipitationeh is the hourly reduction factor for g due to evaporation
)*exp( ph Eke Ep is potential evaporation; k empirical factor found to be k=0.075
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Model description: the fq parameter
))1(
(*3600
a
p
an
p
rL
cerR
E
Rn is net radiation, ra aeodynamic resistance Δe the saturated vapour pressure deficit
gfq *93.01
Input data: (global radiation), cloud cover,windspeed, humidity and temperature
fq is the source strength reduction function due to the moisture content of the road
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Model description: the road dust layer -sources
Two kinds of particles are put into the model:•particles caused by the wear of pavement; •particles from traction sand
sw lll
l total dust layer (0,1)lw road wear dust layer (0,1)ls sand dust layer (0,1)α share of road wear particles in the total dust layerβ share of sand particles in the total dust layer
susprfststtwtw ffakll ),0.1min( 1,,
dayssandingnotifffll
dayssandingifffN
ll
susprftsts
susprftsts
),0.1min(
)1
,0.1min(
1,,
1,,
The road wear dust layer
The sand dust layer
ast share of studded tyres (%)kst normalising factor, 0.03frf hourly decay of road dust layer due to runoff by rain (0,1)fsusp hourly decay of road dust layer due to suspension (0,1)
N number of sanding days during a winter season
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Model description: the road dust layer -sources
The road wear dust layer
The sanding dust layer
Sanding occasions are calculated based onmeteorological data. (Sanding is assumed to occur during slippery conditions).
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Model description: the road dust layer - sinks
qdecaysusp fkf *1fsusp is the relative hourly decay of the dust depot due to suspensionkdecay is an empirical parameter (0.001,decresing the dust layer to half of its value within about one dry month)
a) suspension
b) run-off
)0.,1*)(,0.0max( htt errgRF RF is the hourly water run-off and is used tocalculate the hourly decay of road dust layer due to runoff by rain (0,1) , frf
95.0
)2/()2(*05.01,2
0.12
max
maxmax
rf
rf
rf
fRFrrif
RFRFfRFrrif
frrif
RFmax is maximum hourly water run-off (10 mm)
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Model description: road dust layer and final results
2-Jan 2-M ar 1-M ay 30-Jun 29-Aug 28-O ct 27-D ec
0
0.2
0.4
0.6
0.8
1
dust
laye
r dust layerl tota llw road w ear
ls sand
2-Jan 2-M ar 1-M ay 30-Jun 29-Aug 28-O ct 27-D ec
0
200
400
600
800
1000
ef(d
ust
) m
g/v
km -
mod
el
2 -Jan 2-M ar 1-M ay 30-Jun 29-Aug 28-O ct 27-D ec
0
200
400
600
800
1000
1200
1400
ef(
dus
t) m
g/vk
m -
trac
er m
eth
od Results using also a dispersion model
kmmg/veh216method)tracer
km mg/veh 209model)
(
(
suspensionf
suspensionf
e
e
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Examples of results
Street Height of buildings (m) side 1/side 2
Width of the street
(m)
Traffic intensity
(vehicles/day)
Heavy duty
vehicles (%)
Antiskid treatment
Share of studded
tyres (%)
Sundsvall/Skolhusallen 10/1 20 20000 4 sand 90 Uppsala/Kungsgatan 20/0 18 18000 5 sand 76
Stockholm/Sveavägen 25/25 33 29100 4 salt 75 Stockholm/Norrlandsg 25/25 15 14800 4 salt 75 Stockholm/Hornsgatan 24/24 24 35000 5 salt 75 Malmö/Amiralsgatan 25/25 21 23000 10 salt 30
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Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM10 (µg/m3) at Hornsgatan for the year 2004. (b) The same results as (a) but sorted according to size
Hornsgatan 2004
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1-Jan 1-M ar 30-Apr 29-Jun 28-Aug 27-O ct 26-D ecYe a r 2 0 0 0
0
10
20
30
PM
2.5 s
tree
t-P
M2.
5 ro
of
(µg
/m3 )
N um ber of datapoints=356Average m easured=5.4Average m odelled=5.5r=0.72
1-M ar 15-M ar 29-M ar 12-Apr 26-Apr 10-M ay 24-M ay 7-Jun 21-JunÅr 2 0 0 5
0
5
10
15
PM
2.5 s
tree
t-P
M2
.5ro
of
(µg
/m3 )
N um ber of datapoints=100Average m easured=2.4Average m odelled=2.4r=0.61
a)
b)
The model can also be used for PM2.5 by changing the reference emissionfactor
Comparison of measured (+) and modelled (grey solid line) local contribution to daily mean PM2.5 concentrations (µg m–3) at two different streets in Sweden with different shares of studded tyres: (a) Hornsgatan in Stockholm in 2000 with approximately 75% studded tyres in the winter, (b) Amiralsgatan in Malmö for about four months in 2005 with approximately 30% studded tyres in the winter.
Other results- PM2.5
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1-Jan 1-M ar 30-A pr 29-Jun 28-A ug 27-O ct 26-D ecY e a r 2 0 0 3
0
10
20
30
40
50
PM
10st
reet
-PM
10ro
of (
µg/
m3 ) N um ber o f da ta po in ts= 344
A verage m easured=7 .4A verage m odelled=7 .1r=0 .84
0 10 20 30 40 50
PM 1 0 (m e a su re d -b a ckg ro u n d ) µg /m 3
0
10
20
30
40
50
PM
10
- m
od
elle
d (
µg
/m3 )
m easured- background m odelledyearly m ean 7 .4 7.1 90-percentile 13.2 12.398-percentile 17.2 16.9
PM10 Jagtvej year 2003PM10 Jagtvej year 2003
Comparison of measured (+) and modelled (solid line) daily mean concentrations of PM10 (µg/m3) at Jagtvej / Copenhagen for the year 2003.(b) The same results as (a) but sorted according to size. Model results are based on measured NOx concentrations.
Other results- no studded tyres
kmvehmgeee suspensionf
directf
totalf /129
Ketzel, M., Omstedt, G., Johansson, C., During, I., Pohjola, M., Dieterman,O., Gidhagen, L., Wåhlin,P., Lohmeyer, A., Haakana, M. and Berkowicz, R., 2007: Estimation and validation of PM2.5/PM10 exhaused and non-exhaused emissionfactors for practical street pollution modelling. Atmospheric Environment 41, 9370-9385.
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Type of stations 19 streets and close to roads stations 21 urban background stations
Andersson, S. och Omstedt, G., 2009: Validering av SIMAIR mot mätningar av PM10, NO2 och bensen. Utvärdering för svenska tätorter och trafikmiljöer avseende år 2004 och 2005. SMHI Meteorologi, Nr. 137.
Model validation of SIMAIRroad
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Results- streets and roads
The uncertainty of modelling estimation is defined as the maximum deviation between the measured and calculatedconcentration levels for 90 % ofindividual monitoring points, without taking into account the timing of the events. The average annual modelling uncertaintyfor PM10 is defined as ±50%
Comparison with EU Air Quality Directive targets
MRPE annual mean=0.38 ; MRDE annual mean=0.24MRPE 90-percentile =0.42
p
pp
O
MORPEMRPE
max)max(
Fairmode http://fairmode.ew.eea.europa.eu/
LV
MORDEMRDE LVLV
max)max(
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Improvements
Improve input data; study tyres, wintersanding / salting, street sweeping
• The computation of the sanding days is currently based on the meteorological parameters. This treatment could be improved by using the predictions of so-called road weather models.
• The cleaning of road surfaces is not considered in the present version of the model, although it can potentially have a major impact on the suspension emissions.
• The modelling of the influence of precipitation involves also challenges, especially in case of short-term, spatially limited and weak rain-and snowfall.
Mari Kauthaniemi et al., 2010
Include snow in the model; experimental data from northern Sweden (Umeå) and Finland can help us here
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New parameters
a) Studded tyres
b) Vehicle speed
c) CMA
d) Street sweeping
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new parametersa) studded tyres
referencewearroad
tyresstuddedwithoutfwearroad
tyresstuddedwithoutfref
etyreaetyreaRW
RWg )(%)*/()(%)*()( ,,
Suggestion: include a new function g, where g is a normalised function for the road wear,
use a linear relationship based on Norman and Johansson (2006) for dry conditions
wearroadtyresstuddedwithoutPMe , the road wear without studded tyres, tyre(%) is the relative proportion of
cars using studded tyres and a is a constant.
Typical values:
wearroadtyresstuddedwithoutPMe , 49 mg/ veh km
g with 75% studded tyres 1
g with 30% studded tyres 0.54
g with 0 % studded tyres 0.23
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Process Emission factor (mg vkm–1)
Notes
Exhaust 25–79 Share of heavy duty vehicles varies between 2 and 20%
Road wear, studded tyres 99–240 Share of studded tyre use varies between 45 and 92%
Road wear, un-studded tyres 36–57 Vehicle component wear estimated at 10 mg vkm–1
Winter sanding/salting 50–61 Uncertain estimation
Vehicle components wear 8–12 Share of heavy duty vehicles varies between 2 and 20%
http://simair.smhi.se/luftkvalitet/documents/meteorologi_134.pdf
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new parametersb) vehicle speed
)(refV
Vf
include a new function f, where V is vehicle speed and Vref
is the reference vehicle speed
What do we know about f ?
Measurements in lab. (Gustafsson et al,2005): an increase by a factor of 4 for an increase in vehicle speed from 40 to 80 km/h for studded tyres. Omstedt et al. (2005)similar emission factors for a Swedish highway (110 km/h) as those for Hornsgatan( 40 km/h)
Johansson et al.(2008):an increase in emission factors of about 20% from 40 km/h (streets inside Stockholm) to 80 km/h( Essingeleden)
Similar results from other studies, emission factors for motorways are not extremely highcompare to emission factors for city streets
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new parametersc) CMA
What do we know about CMA?timescale,..
•keep the surface wet for same days or include a new parameter similar to fq
•do some sensitivity analyses to find reasonable time scales
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new parametersd) street sweeping
•decrease the dust layer at selected time (probably rather different results due to selected time)
•check the timescale for the increase of the dust layer due to studded tyres
•do some sensitivity analyses
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Databases and models used by the SIMAIR system
Gidhagen, L., Johansson, H. and Omstedt, G., 2009: SIMAIR - Evaluation tool for meeting the EU directive on air pollution limits. Atmospheric Environment, 43, 1029-1036.Omstedt,G., Andersson,S., Gidhagen, L. and Robertson, L., 2010. New model tools for meeting the targets of the EU Air Quality Directive: description, validation and evaluation of local air quality improvement due to reduction of studded tyre use on Swedish roads. Accepted ( Int. J. Environment and Pollution)